Abstract #0153

Brain Tissue Differentiation Using Diffusion-Relaxation Correlation Spectrum Imaging with a Spectral Weighting-Based Clustering Method

Guowen Shao^1,2,3, Francesco Sanvito^1,2, Zexi Wang^1,2, Won Kim⁴, Holden H. Wu^2,3, Benjamin M. Ellingson^1,2,3,4, Dan Ruan^3,5, and Jingwen Yao^1,2,3

¹Brain Tumor Imaging Laboratory (BTIL), Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States, ³Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, United States, ⁴Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States, ⁵Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Multi-Contrast, Multi-Contrast, DRCSI, Clustering

Motivation: Diffusion-Relaxation Correlation Spectroscopic Imaging (DR-CSI) holds promise for differentiating microscopic tissue components, but challenges remain in accurately resolving spectral peaks and distinguishing specific tissue components.

Goal(s): To assess the feasibility of a spectral weighting-based clustering method for partitioning DR-CSI spectra and generating fractional maps for each identified component, improving differentiation of sub-voxel tissue components.

Approach: The method was evaluated on a digital phantom simulation across varying signal-to-noise ratios and validated with data from healthy volunteers and brain tumor patients to assess clinical applicability.

Results: Our method effectively partitioned components and generated corresponding fractional maps in both phantom and human data.

Impact: The integration of DR-CSI spectra with spectral weighting-based clustering enables automated and precise differentiation of tissue components and sub-voxel tissue characterization. This approach shows promise in distinguishing challenging tumor types, potentially transforming brain tumor diagnostics and treatment planning.

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